Optical Signal Parameter Analysis Model Training
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Solution Overview
Problem
In digital coherent optical transmission technology, constructing a mathematical model with high precision to analyze the influence of parameters on communication settings is challenging due to the variability of optical signals, which can lead to degraded transmission quality, especially for individuals with insufficient knowledge and experience.
Innovation Solution
A parameter analysis method and apparatus that trains a behavior model to output an index value for optical signals, using a change generating unit to simulate variations in signal characteristics, such as polarization state and frequency offset, to enhance model precision and adapt to various conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a mathematical model is trained using actual optical signals, then the model can analyze influences of parameters on transmission processing, but the model precision is insufficient due to variability in optical signal characteristics
Solution Approach 1:
The patent applies preliminary action by generating artificial optical signals with predetermined characteristics before model training. Instead of directly training with variable real optical signals, the system pre-generates training data with controlled parameters (polarization state, frequency offset, amplitude, phase) to establish a robust baseline model that can then adapt to real-world variations.
Solution Approach 2:
The patent systematically changes key parameters of artificial optical signals including polarization state, frequency offset, amplitude, and phase during training. By deliberately varying these parameters across multiple training iterations, the model learns to recognize patterns and maintain precision despite signal variability, directly addressing the contradiction between precision and adaptability.
2Ease of operation
If communication settings are configured by persons with insufficient knowledge and experience, then the system can be operated with reduced expertise requirements, but transmission quality is degraded
Solution Approach 1:
The patent implements self-service through automated parameter analysis and optimization. The trained model automatically evaluates communication settings, identifies optimal parameter combinations, and provides recommendations without requiring deep expert knowledge. This allows operators with limited expertise to configure systems while maintaining high transmission quality, resolving the contradiction between ease of operation and reliability.
Solution Approach 2:
The system incorporates feedback mechanisms where the model continuously analyzes transmission quality metrics and adjusts parameter recommendations accordingly. This closed-loop approach ensures that even inexperienced operators can achieve optimal settings through model-guided feedback, maintaining reliability while improving ease of operation.
3Productivity
If the model is trained with fixed optical signal characteristics, then training can be completed in realistic time periods, but the model cannot adapt to various conditions of optical components and fiber
Solution Approach 1:
The patent segments the training process into multiple phases: initial training with artificial signals having fixed characteristics for rapid convergence, followed by fine-tuning with varied parameters to improve adaptability. This segmented approach allows the model to achieve basic precision quickly while still adapting to various component conditions, resolving the contradiction between training speed and adaptability.
Solution Approach 2:
The training dataset dynamically evolves from static artificial signals to more complex variations. The system starts with simplified signal models for fast training, then progressively introduces variability in polarization, frequency offset, and other parameters, allowing the model to adapt to different component conditions while maintaining reasonable training timeframes.
Data Source
AI summary
A parameter analysis method executable by a computer, the method includes training a model configured to output an index value relating to a characteristic of an optical signal, and changing the characteristic of the optical signal usable for training the model.